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Nº de artículo:
104729386
Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
89% of respondents would recommend this to a friend
Nº de artículo:
104729386
GTQ 617
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This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models.
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What Stands Out
Comprehensive Coverage
This book offers an in-depth exploration of time series forecasting techniques, integrating both machine learning and deep learning approaches using PyTorch and pandas, catering to industry needs.
Hands-On Approach
Readers engage in practical, hands-on projects to solidify concepts, ensuring skills are directly applicable in real-world scenarios, making it invaluable for professionals looking to enhance their forecasting prowess.
Future-Proof Skills
By focusing on modern tools and methodologies in time series analysis, the book equips readers with relevant skills necessary for evolving industry demands, thereby enhancing their career prospects in data science.
Detalles de producto
- Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architecturesGet With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader FreeKey FeaturesApply ML and global models to improve forecasting accuracy through practical examplesEnhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATSLearn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressionsBook DescriptionPredicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.What you will learnBuild machine learning models for regression-based time series forecastingApply powerful feature engineering techniques to enhance prediction accuracyTackle common challenges like non-stationarity and seasonalityCombine multiple forecasts using ensembling and stacking for superior resultsExplore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time seriesEvaluate and validate your forecasts using best practices and statistical metricsWho this book is forThis book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.Table of ContentsIntroducing Time SeriesAcquiring and Processing Time Series DataAnalyzing and Visualizing Time Series DataSetting a Strong Baseline Forecast Time Series Forecasting as Regression Feature Engineering for Time Series ForecastingTarget Transformations for Time Series Forecasting Forecasting Time Series with Machine Learning Models Ensembling and StackingGlobal Forecasting Models Introduction to Deep LearningBuilding Blocks of Deep Learning for Time Series(N.B. Please use the Read Sample option to see further chapters)
| Publisher | Packt Publishing |
| Publication date | 31 Oct. 2024 |
| Edition | 2nd |
| Language | English |
| Print length | 658 pages |
| ISBN-10 | 1835883184 |
| ISBN-13 | 978-1835883181 |
| Item weight | 1.12 kg |
| Dimensions | 19.05 x 3.78 x 23.5 cm |
Who Should Buy?
-
Data Scientists
Ideal for data scientists looking to enhance their skills in time series analysis with advanced machine learning techniques.
-
Machine Learning Engineers
Perfect for professionals seeking to apply deep learning methodologies to time series forecasting projects in various industries.
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Students and Learners
Beneficial for students studying data science who want practical knowledge of time series analysis using Python, PyTorch, and pandas.
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Beginners
Not suitable for complete beginners, as prior knowledge of Python and basic statistics is typically needed to grasp concepts.
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Preguntas y respuestas de los clientes
-
Pregunta:
What prior knowledge is needed to use this book?
Respuesta: A basic understanding of statistics and Python programming is recommended. -
Pregunta:
Is this book suitable for beginners?
Respuesta: Yes, it starts with fundamental concepts before progressing to advanced topics. -
Pregunta:
Can I use this book for practical applications in my job?
Respuesta: Absolutely, it provides industry-ready techniques and strategies for real-world forecasting problems.
Higher Education Editorial Review
**** "Modern Time Series Forecasting with Python" emerges as a highly recommended resource for those delving into machine learning and deep learning applications in time series analysis. Users have expressed their appreciation for the book's layered approach to presenting complex data analysis concepts, which facilitates understanding and practical application. Its usefulness is especially noted within the financial sector, where clients have sought the author's expertise in Python for numerical analysis and tick data handling. However, some users have pointed out limitations in the book's visual presentation, particularly noting the lack of color in charts and visual aids, which may hinder comprehension for readers who rely on graphical illustrations for better understanding. Despite this, the overwhelming sentiment among users is positive, highlighting the book's effectiveness in equipping readers with the necessary knowledge and skills to deliver results in time series forecasting. Overall, "Modern Time Series Forecasting with Python" serves as a valuable tool for both beginners and experienced practitioners in the field, with its comprehensive content significantly aiding users in their time series data projects. **
Customer Reviews & Ratings
31 valoraciones de los clientes
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5 estrella
73%
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4 estrella
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3 estrella
5%
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2 estrella
4%
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ventajas
- Layered approach makes complex concepts easier to understand.
- Highly useful for financial projects and tick data analysis.
- Strong recommendation from users as an invaluable resource.
Contras
- Lack of colors in charts may make them hard to read.
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características y beneficios
- Learn traditional and advanced techniques for time series forecasting.
- Hands-on practical examples to improve forecasting accuracy.
- Includes free eBook with print or Kindle purchase.
- Covers deep learning models including RNNs and transformers.
- Ideal for professionals and students in various industries.
- New edition features enhancements in transformer architectures and probabilistic forecasting.
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